Processing of musical and vocal emotions through cochlear implants
Bibliographic record
Abstract
Cochlear implants (CI) partially restore hearing in the deaf. However, the ability to recognize emotions in speech and music is limited due to the implant's technological limitations and the impaired neural pathways that developed after sensorineural hearing loss. This leads to developmental and socioeconomic problems for CI-users and thus a decrease in quality of life. Behavioural and neural correlates of this deficit are not yet well established. This thesis aims to characterize the effect of CIs on auditory emotion perception and, for the first time, to directly compare vocal and musical emotion perception through a CI-simulator. The thesis investigated the ability of normal hearing individuals to perceive basic emotions in CI-simulated vocal and musical sounds, using a behavioural task and electroencephalography (EEG). In the behavioural study, the perception of musical and vocal emotions was impaired in the CI-simulated condition. Perception was correlated with timbral acoustic cues. In the EEG study, the averaged event-related potentials' components had reduced amplitudes and delayed latency as early as 50 milliseconds in the CI-simulated condition. Using this previously validated neuro-behavioural approach with CI-users can further enhance our knowledge and prove the importance of timbral acoustical cues for emotion recognition. It can lead to developing new processing strategies that capitalize on these cues, leading to better perception of auditory emotions and thus improving the quality of life for CI-users.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".